ArticleJournal of clinical medicine2022
Applying an Improved Stacking Ensemble Model to Predict the Mortality of ICU Patients with Heart Failure.
Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of machine learning algorithms for mortality risk, readmission and phenotype prediction in patients with heart failure: exploring key data sources, input variables and outcomes.BMC medical informatics and decision making · 2026Pooled it
- Mitigating Clinical Confounding in AI Models: A Comparative Analysis of Associative and Causal-Fused AI for ICU Readmissions.Healthcare (Basel, Switzerland) · 2026Article
- Atherogenic index of plasma and risk of acute kidney injury in critically ill patients: a multi-cohort study with machine learning and SHAP analysis.Renal failure · 2025Article
- Evolution of Research on Artificial Intelligence for Heart Failure: A Bibliometric and Visual Analysis.Journal of multidisciplinary healthcare · 2025Review
- An interpretability heart disease prediction model based on stacking ensemble with SHAP.Frontiers in molecular biosciences · 2025Article
- Predicting in-hospital mortality in patients with heart failure combined with atrial fibrillation using stacking ensemble model: an analysis of the medical information mart for intensive care IV (MIMIC-IV).BMC medical informatics and decision making · 2024Article
- Analysis and prediction of infectious diseases based on spatial visualization and machine learning.Scientific reports · 2024Article
- Machine learning-based prognostic model for 30-day mortality prediction in Sepsis-3.BMC medical informatics and decision making · 2024Article
- A stacked machine learning-based classification model for endometriosis and adenomyosis: a retrospective cohort study utilizing peripheral blood and coagulation markers.Frontiers in digital health · 2024Article
- Prediction of clinical efficacy of acupuncture intervention on upper limb dysfunction after ischemic stroke based on machine learning: a study driven by DSA diagnostic reports data.Frontiers in neurology · 2024Article
- Cardiac disease prediction using AI algorithms with SelectKBest.Medical & biological engineering & computing · 2023Article
- Construction of the machine learning-based live birth prediction models for the first in vitro fertilization pregnant women.BMC pregnancy and childbirth · 2023Article
- Heart Failure Emergency Readmission Prediction Using Stacking Machine Learning Model.Diagnostics (Basel, Switzerland) · 2023Article
- Machine Learning and Causal Approaches to Predict Readmissions and Its Economic Consequences Among Canadian Patients With Heart Disease: Retrospective Study.JMIR formative research · 2023Article
- Article
- Leveraging ensemble learning for predicting 30-day readmission in heart failure ICU patients.Digital healthArticle
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiovascular diseases have been identified as one of the top three causes of death worldwide, with onset and deaths mostly due to heart failure (HF). In ICU, where patients with HF are at increased risk of death and consume significant medical resources, early and accurate prediction of the time of death for patients at high risk of death would enable them to receive appropriate and timely medical care. The data for this study were obtained from the MIMIC-III database, where we collected vital signs and tests for 6699 HF patient during the first 24 h of their first ICU admission. In order to predict the mortality of HF patients in ICUs more precisely, an integrated stacking model is proposed and applied in this paper. In the first stage of dataset classification, the datasets were subjected to first-level classifiers using RF, SVC, KNN, LGBM, Bagging, and Adaboost. Then, the fusion of these six classifier decisions was used to construct and optimize the stacked set of second-level classifiers. The results indicate that our model obtained an accuracy of 95.25% and AUROC of 82.55% in predicting the mortality rate of HF patients, which demonstrates the outstanding capability and efficiency of our method. In addition, the results of this study also revealed that platelets, glucose, and blood urea nitrogen were the clinical features that had the greatest impact on model prediction. The results of this analysis not only improve the understanding of patients' conditions by healthcare professionals but allow for a more optimal use of healthcare resources.
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